https://community.databricks.com/t5/announcements/agentic-data-engineering-with-genie-code-and-lakeflow/m-p/156577#M792 <P><STRONG>Databricks</STRONG><SPAN> is bringing </SPAN><STRONG>Genie Code</STRONG><SPAN> deeper into </SPAN><STRONG>Lakeflow</STRONG><SPAN>, giving data engineers an AI partner built for data that can help with pipeline development, job orchestration, monitoring, and debugging in one experience.</SPAN></P> <P><FONT size="4"><STRONG>Key highlights</STRONG></FONT></P> <UL> <LI aria-level="1"><STRONG>Build pipelines in plain English: Genie Code</STRONG><SPAN> can generate and update </SPAN><STRONG>Spark Declarative Pipelines with ingestion</STRONG><SPAN>, transformations, and data quality checks from a natural language prompt.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Orchestrate jobs faster: </STRONG><SPAN>It works with </SPAN><STRONG>Lakeflow Jobs</STRONG><SPAN> to help define tasks, dependencies, scheduling, and monitoring without manually stitching everything together.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Work with the tools data engineers already use: </STRONG><SPAN>Genie Code can help extend workflows with CDC patterns, Auto Loader, expectations, and Declarative Automation Bundles for </SPAN><STRONG>CI/CD-friendly projects</STRONG><SPAN>.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Debug with more context: </STRONG><SPAN>It can inspect pipeline behavior, analyze failures, propose fixes, and show diffs before changes are applied, helping teams move faster while staying in control.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Adapt to your environment: </STRONG><SPAN>Teams can customize Genie Code with instructions, skills, and MCP connections to bring in domain knowledge, external tools, and internal workflows.</SPAN></LI> </UL> <P class="p8i6j01 paragraph"><A style="background-color: #ff3621; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; font-weight: bold; display: inline-block;" href="https://www.databricks.com/blog/agentic-data-engineering-genie-code-and-lakeflow?utm_source=bambu&amp;utm_medium=social&amp;utm_campaign=advocacy" target="_blank" rel="noopener"><span class="lia-unicode-emoji" title=":backhand_index_pointing_right:">👉</span> Read the full post here </A></P> Mon, 11 May 2026 12:24:00 GMT Tushar_Parekar 2026-05-11T12:24:00Z https://community.databricks.com/t5/announcements/agentic-data-engineering-with-genie-code-and-lakeflow/m-p/156577#M792 <P><STRONG>Databricks</STRONG><SPAN> is bringing </SPAN><STRONG>Genie Code</STRONG><SPAN> deeper into </SPAN><STRONG>Lakeflow</STRONG><SPAN>, giving data engineers an AI partner built for data that can help with pipeline development, job orchestration, monitoring, and debugging in one experience.</SPAN></P> <P><FONT size="4"><STRONG>Key highlights</STRONG></FONT></P> <UL> <LI aria-level="1"><STRONG>Build pipelines in plain English: Genie Code</STRONG><SPAN> can generate and update </SPAN><STRONG>Spark Declarative Pipelines with ingestion</STRONG><SPAN>, transformations, and data quality checks from a natural language prompt.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Orchestrate jobs faster: </STRONG><SPAN>It works with </SPAN><STRONG>Lakeflow Jobs</STRONG><SPAN> to help define tasks, dependencies, scheduling, and monitoring without manually stitching everything together.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Work with the tools data engineers already use: </STRONG><SPAN>Genie Code can help extend workflows with CDC patterns, Auto Loader, expectations, and Declarative Automation Bundles for </SPAN><STRONG>CI/CD-friendly projects</STRONG><SPAN>.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Debug with more context: </STRONG><SPAN>It can inspect pipeline behavior, analyze failures, propose fixes, and show diffs before changes are applied, helping teams move faster while staying in control.</SPAN></LI> </UL> <UL> <LI aria-level="1"><STRONG>Adapt to your environment: </STRONG><SPAN>Teams can customize Genie Code with instructions, skills, and MCP connections to bring in domain knowledge, external tools, and internal workflows.</SPAN></LI> </UL> <P class="p8i6j01 paragraph"><A style="background-color: #ff3621; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px; font-weight: bold; display: inline-block;" href="https://www.databricks.com/blog/agentic-data-engineering-genie-code-and-lakeflow?utm_source=bambu&amp;utm_medium=social&amp;utm_campaign=advocacy" target="_blank" rel="noopener"><span class="lia-unicode-emoji" title=":backhand_index_pointing_right:">👉</span> Read the full post here </A></P> Mon, 11 May 2026 12:24:00 GMT https://community.databricks.com/t5/announcements/agentic-data-engineering-with-genie-code-and-lakeflow/m-p/156577#M792 Tushar_Parekar 2026-05-11T12:24:00Z